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computational proteomics, bioinformatics, machine learning, computer science, chemometrics, analytical chemistry, or a closely related field Strong experience in machine learning and/or deep learning
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, and open scientific resources where relevant Required Qualifications Applicants must have: A PhD in computational proteomics, bioinformatics, machine learning, computer science, chemometrics
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programming in Python, R, or similar languages Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to
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, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages Background in machine learning or deep
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and
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learning research, possessing a combination of mathematical maturity and advanced engineering skills: Education: A PhD in Computer Science, Mathematics, Statistics, or Theoretical Physics at the time of
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structured, well-documented way that supports benchmarking, reproducibility, and reuse by others. Essential: Strong data analysis and machine learning skills and experience with PyTorch (or equivalent
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postdocs positions in the Center for Basic Research in Program Verification (CPV) at Department of Computer Science, Aarhus University. The research topics at the Center for Basic Research in Program
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which you will be automatically redirected to an external site after 5 seconds) Save job Click to add the job to your shortlist Send job Job Details Are you interested in using machine learning to
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Postdoc in first-principles modelling and machine-learned force fields for correlated disorder in...
Chemistry at the Institute of Materials Chemistry, TU Wien. The group develops and applies electronic-structure theory, machine-learning methods and predictive calculations of defects and transport properties